Changes in version 0.1.0 (2026-07-02)                  

Initial release. deaviz provides high-dimensional visualization methods
for Data Envelopment Analysis (DEA), built around a single validated
dea_data() object and following a compute_*() / plot_*() naming
convention.

Data object

  - dea_data() constructs the validated input/output object that every
    function consumes; as_dea_data() coerces existing data. print()
    methods are provided for the dea_data and dea_som classes.

Analysis

  - compute_efficiency() --- radial DEA efficiency scores (CRS, VRS,
    DRS, IRS or FDH; input- or output-oriented).
  - compute_cross_efficiency(), compute_cross_efficiency_weights() and
    standardize_weights() --- cross-efficiency scores and their weight
    profiles.
  - compute_multiplier_weights() --- optimal input/output multipliers.
  - compute_som() --- a self-organizing map of the input/output
    profiles.

Visualization

  - Data and efficiency overview: plot_io_distributions(),
    plot_efficiency_distributions(), plot_io_efficients(),
    plot_io_scatter(), plot_io_heatmap().
  - Frontier and projections: plot_io_costa_frontier(),
    plot_io_pca_biplot(), plot_io_mds(), plot_io_3dscatter().
  - Benchmarking networks: plot_io_lambda_network(),
    plot_io_peer_network().
  - Cross-efficiency: plot_cem_heatmap(), plot_cem_unfolding(),
    plot_cem_weights_heatmap().
  - Profiles: plot_io_radar() (with its coord_radar() coordinate system)
    and plot_io_parcoo().
  - Self-organizing maps: plot_io_som(), plot_io_som_components().
  - Panel data: plot_panel_io_biplot() draws each DMU's trajectory over
    time.

Cross-cutting features

  - Passing a single DMU to labels fades the rest of the plot into a
    focus view; the fade argument tunes the level or disables it.
  - x_angle rotates long x-axis tick labels on plot_io_distributions(),
    plot_io_heatmap(), plot_io_parcoo(), plot_cem_heatmap() and
    plot_cem_weights_heatmap().
  - Many plots accept interactive = TRUE to return a plotly widget.
  - Consistent, colour-blind-safe style throughout: the Okabe-Ito
    qualitative palette, a viridis sequential palette, and a shared
    minimal theme.

Datasets

  - chinese_cities --- 35 Chinese cities with three inputs and three
    outputs (Sueyoshi, 1992).
  - taiwanese_banks --- a balanced panel of 22 Taiwanese commercial
    banks over 2009-2011 (Kao & Liu, 2014), the worked example for
    plot_panel_io_biplot().